Data Science in the News

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Lab team studies calibrated AI and deep learning models to more reliably diagnose and treat disease

May 29, 2020 - 
A team led by LLNL computer scientist Jay Thiagarajan has developed a new approach for improving the reliability of artificial intelligence and deep learning-based models used for critical applications, such as health care. Thiagarajan recently applied the method to study chest X-ray images of patients diagnosed with COVID-19, arising due to the novel SARS-Cov-2 coronavirus. Read more at LLNL...

AI identifies change in microstructure in aging materials

May 26, 2020 - 
LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Read more at LLNL News.

COVID-19 research goes public through new portal

May 18, 2020 - 
A new online data portal is making available to the public a wealth of data LLNL scientists have gathered from their ongoing COVID-19 molecular design projects, particularly the computer-based “virtual” screening of small molecules and designed antibodies for interactions with the SARS-CoV-2 virus for drug design purposes. The portal houses a wealth of data LLNL scientists have gathered from...

Interpretable AI in healthcare (PODCAST)

May 17, 2020 - 
LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50. Listen at Data Skeptic.

The incorporation of machine learning into scientific simulations at LLNL (VIDEO)

May 5, 2020 - 
In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory." Read more and watch the video at insideHPC.

New ML platform generates novel COVID-19 antibody sequences for experimental testing

May 1, 2020 - 
LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2, the virus that causes COVID-19. The research team is performing experimental testing on the chosen antibody designs. Read more at LLNL News.

New partnership results in increased access to compelling 'real world data'

April 21, 2020 - 
Through a new partnership between the UC San Diego Library, Halıcıoğlu Data Science Institute (HDSI), and LLNL's Data Science Institute, UCSD library patrons can now access and analyze two new “real world” data sets from LLNL. The Open Data Initiative collection shares some of LLNL’s challenging and unique data sets, which range in complexity from large-scale, domain-specific simulated data...

Upgrades for LLNL supercomputer from AMD, Penguin Computing aid COVID-19 research

April 21, 2020 - 
Under a new agreement, AMD will supply upgraded graphics accelerators for LLNL’s Corona supercomputing cluster, expected to nearly double the system’s peak compute power. The system will be used by scientists through the public/private COVID-19 HPC Consortium, and by LLNL researchers, who are working on discovering potential antibodies and anti-viral compounds for SARS-CoV-2, the virus that...

Local Women in Data Science conference showcases Lab research

April 3, 2020 - 
For the third consecutive year, LLNL hosted a Women in Data Science (WiDS) regional event on March 2. The event drew dozens of attendees from LLNL, Sandia National Laboratories, local universities, and Bay Area commercial companies. Livermore was one of over 200 regional events in 60 countries coordinated with the main WiDS conference at Stanford University. According to the WiDS website...

LLNL creates web resources to aid in fight against COVID-19

March 30, 2020 - 
LLNL is fully committed to helping protect the U.S. from COVID-19 and to speed the recovery of those affected. As a world-class research institute, we have considerable infrastructure, unique research capabilities and a dedicated team of scientists and engineers supporting the fight against the COVID-19 pandemic. Our current COVID-19 research and response activities are focused on four broad...

New partnership to unleash U.S. supercomputing resources in the fight against COVID-19

March 26, 2020 - 
The White House announced the launch of the COVID-19 High Performance Computing Consortium to provide COVID-19 researchers worldwide with access to the world’s most powerful high performance computing resources that can significantly advance the pace of scientific discovery in the fight to stop the virus. Read more at LLNL News.

Lab antibody, anti-viral research aids COVID-19 response

March 26, 2020 - 
LLNL scientists are contributing to the global fight against COVID-19 by combining artificial intelligence/machine learning, bioinformatics and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs to combat the disease. Armed with the virus’ predicted 3D structure and a few antibodies known to bind and neutralize SARS, an LLNL team led by Daniel Faissol and...

DSI sponsors LLNL hackathon

Feb. 18, 2020 - 
Since 2012, Lawrence Livermore National Laboratory’s (LLNL’s) Computing directorate has held hackathons three times a year. These spring, summer, and fall events are scheduled over 24 hours and invite software teams to work on new ideas, programming languages, open-source tools, or project tasks. Exploration and experimentation are highly encouraged, and “It’s OK to fail” is the event mantra...

Deep learning may provide solution for efficient charging, driving of autonomous electric vehicles

Feb. 4, 2020 - 
LLNL computer scientists and software engineers have developed a deep learning-based strategy to maximize electric vehicle (EV) ride-sharing services while reducing carbon emissions and the impact to the electrical grid, emphasizing autonomous EVs capable of offering 24-hour service. Read more at LLNL News.

Lab leads effort to model proteins tied to cancer

Oct. 31, 2019 - 
Computational scientists, biophysicists and statisticians from LLNL and Los Alamos National Laboratory(LANL) are leading a massive multi-institutional collaboration that has developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancer. Read more at LLNL News.

Successful simulation and visualization coupling proves the power of Sierra

Oct. 22, 2019 - 
As the first National Nuclear Security Administration (NNSA) production supercomputer backed by GPU- (graphics processing unit) accelerated architecture, Sierra’s acquisition required a fundamental shift in how scientists at Lawrence Livermore National Laboratory (LLNL) program their codes to take advantage of the GPUs. The majority of Sierra’s computational power—95 percent of its 125...

Cindy Gonzales forges a new career in data science

Sept. 25, 2019 - 
Through LLNL’s Data Science Immersion Program, Gonzales is now among the Lab’s newest data scientists. For two and a half years, she juggled a demanding workload—coordinating Computing’s Scholar Program, interning with data scientists, learning from mentors, supporting LLNL’s Data Science Institute, and attending college part time—while also having her first child. Read more at LLNL Computing...

LLNL Center for Applied Scientific Computing: accelerating scientific discovery (VIDEO)

July 12, 2019 - 
The Center for Applied Scientific Computing (CASC) serves as LLNL’s window to the broader computer science, computational physics, applied mathematics, and data science research communities. Major thrust areas in CASC research include: (1) Increasing simulation fidelity by integrating multi-physics and multi-scale models, increasing resolution through advanced numerical methods and more...

NFL comes to Lab to hear latest on TBI research

June 5, 2019 - 
Officials from the National Football League visited LLNL to hear how the Department of Energy’s national laboratories are using high-performance computing and artificial intelligence to advance scientific understanding of traumatic brain injury (TBI). Read more at LLNL News.

Speech generation: siblings collaborate on machine learning hackathon project

May 28, 2019 - 
The first recording that brothers Sam and Joe Eklund, along with their colleague Travis Chambers, played for the audience was a validation. “I endorse Travis as president of the United States of America,” the audio clip played, in a voice resembling Barack Obama’s. The second, in the same voice, was a declaration: “Ice is back, our brand new invention” (from the song “Ice Ice Baby” by...